Lung Cancer Staging and Prognosis
Gavitt A Woodard1, Kirk D Jones2, David M Jablons3
1Department of Surgery, University of California, San Francisco, 500 Parnassus Avenue, Room MUW-424, San Francisco, CA, 94143-1724, USA.
Cancer Treatment and Research
|August 19, 2016
Summary
The seventh edition of the non-small cell lung cancer (NSCLC) TNM staging system provides crucial survival data. Future prognostication will integrate TNM staging with molecular profiling for personalized NSCLC outcomes.
Area of Science:
- Oncology
- Cancer Staging
- Molecular Diagnostics
Background:
- The International Association for the Staging of Lung Cancer (IASLC) developed the seventh edition TNM staging system for non-small cell lung cancer (NSCLC).
- TNM staging is a primary prognostic factor for NSCLC recurrence and survival, with 5-year survival rates varying significantly by stage.
Purpose of the Study:
- To present the data-derived TNM classifications for NSCLC from the IASLC Lung Cancer Staging Project.
- To highlight the prognostic significance of TNM stage and explore emerging molecular markers for improved outcome prediction.
Main Methods:
- Analysis of a large international dataset to develop TNM classifications.
- Review of current research on molecular markers, including immunohistochemistry, microarray, and mutation profiling.
- Evaluation of PD-L1 as a potential predictive and prognostic biomarker in NSCLC.
Main Results:
- TNM stage is the most critical prognostic factor in NSCLC, followed by histologic grade, sex, age, and performance status.
- Despite extensive research, few molecular prognostic markers have achieved clinical adoption.
- PD-L1 shows potential as a predictive marker for immunotherapy response but may indicate poorer overall survival.
Conclusions:
- The TNM staging system remains fundamental for NSCLC prognostication.
- Future NSCLC outcome prediction will likely combine TNM staging with molecular tumor profiling for more precise, individualized estimates.
- This integrated approach promises to refine treatment algorithms and improve patient outcomes.

